Microsoft Malware detection¶
1.Business/Real-world Problem
1.1. What is Malware?
The term malware is a contraction of malicious software. Put simply, malware is any piece of software that was written with the intent of doing harm to data, devices or to people.
Source: https://www.avg.com/en/signal/what-is-malware
1.2. Problem Statement
In the past few years, the malware industry has grown very rapidly that, the syndicates invest heavily in technologies to evade traditional protection, forcing the anti-malware groups/communities to build more robust softwares to detect and terminate these attacks. The major part of protecting a computer system from a malware attack is to identify whether a given piece of file/software is a malware.
1.3 Source/Useful Links
Microsoft has been very active in building anti-malware products over the years and it runs it’s anti-malware utilities over 150 million computers around the world. This generates tens of millions of daily data points to be analyzed as potential malware. In order to be effective in analyzing and classifying such large amounts of data, we need to be able to group them into groups and identify their respective families.
This dataset provided by Microsoft contains about 9 classes of malware.
,
Source: https://www.kaggle.com/c/malware-classification
1.4. Real-world/Business objectives and constraints.
- Minimize multi-class error.
- Multi-class probability estimates.
- Malware detection should not take hours and block the user's computer. It should fininsh in a few seconds or a minute.
2. Machine Learning Problem
2.1. Data
2.1.1. Data Overview
- .asm file (read more: https://www.reviversoft.com/file-extensions/asm)
- .bytes file (the raw data contains the hexadecimal representation of the file's binary content, without the PE header)
- Ramnit
- Lollipop
- Kelihos_ver3
- Vundo
- Simda
- Tracur
- Kelihos_ver1
- Obfuscator.ACY
- Gatak
2.1.2. Example Data Point
.asm file
.text:00401000 assume es:nothing, ss:nothing, ds:_data, fs:nothing, gs:nothing .text:00401000 56 push esi .text:00401001 8D 44 24 08 lea eax, [esp+8] .text:00401005 50 push eax .text:00401006 8B F1 mov esi, ecx .text:00401008 E8 1C 1B 00 00 call ??0exception@std@@QAE@ABQBD@Z ; std::exception::exception(char const * const &) .text:0040100D C7 06 08 BB 42 00 mov dword ptr [esi], offset off_42BB08 .text:00401013 8B C6 mov eax, esi .text:00401015 5E pop esi .text:00401016 C2 04 00 retn 4 .text:00401016 ; --------------------------------------------------------------------------- .text:00401019 CC CC CC CC CC CC CC align 10h .text:00401020 C7 01 08 BB 42 00 mov dword ptr [ecx], offset off_42BB08 .text:00401026 E9 26 1C 00 00 jmp sub_402C51 .text:00401026 ; --------------------------------------------------------------------------- .text:0040102B CC CC CC CC CC align 10h .text:00401030 56 push esi .text:00401031 8B F1 mov esi, ecx .text:00401033 C7 06 08 BB 42 00 mov dword ptr [esi], offset off_42BB08 .text:00401039 E8 13 1C 00 00 call sub_402C51 .text:0040103E F6 44 24 08 01 test byte ptr [esp+8], 1 .text:00401043 74 09 jz short loc_40104E .text:00401045 56 push esi .text:00401046 E8 6C 1E 00 00 call ??3@YAXPAX@Z ; operator delete(void *) .text:0040104B 83 C4 04 add esp, 4 .text:0040104E .text:0040104E loc_40104E: ; CODE XREF: .text:00401043j .text:0040104E 8B C6 mov eax, esi .text:00401050 5E pop esi .text:00401051 C2 04 00 retn 4 .text:00401051 ; ---------------------------------------------------------------------------
.bytes file
00401000 00 00 80 40 40 28 00 1C 02 42 00 C4 00 20 04 20 00401010 00 00 20 09 2A 02 00 00 00 00 8E 10 41 0A 21 01 00401020 40 00 02 01 00 90 21 00 32 40 00 1C 01 40 C8 18 00401030 40 82 02 63 20 00 00 09 10 01 02 21 00 82 00 04 00401040 82 20 08 83 00 08 00 00 00 00 02 00 60 80 10 80 00401050 18 00 00 20 A9 00 00 00 00 04 04 78 01 02 70 90 00401060 00 02 00 08 20 12 00 00 00 40 10 00 80 00 40 19 00401070 00 00 00 00 11 20 80 04 80 10 00 20 00 00 25 00 00401080 00 00 01 00 00 04 00 10 02 C1 80 80 00 20 20 00 00401090 08 A0 01 01 44 28 00 00 08 10 20 00 02 08 00 00 004010A0 00 40 00 00 00 34 40 40 00 04 00 08 80 08 00 08 004010B0 10 00 40 00 68 02 40 04 E1 00 28 14 00 08 20 0A 004010C0 06 01 02 00 40 00 00 00 00 00 00 20 00 02 00 04 004010D0 80 18 90 00 00 10 A0 00 45 09 00 10 04 40 44 82 004010E0 90 00 26 10 00 00 04 00 82 00 00 00 20 40 00 00 004010F0 B4 00 00 40 00 02 20 25 08 00 00 00 00 00 00 00 00401100 08 00 00 50 00 08 40 50 00 02 06 22 08 85 30 00 00401110 00 80 00 80 60 00 09 00 04 20 00 00 00 00 00 00 00401120 00 82 40 02 00 11 46 01 4A 01 8C 01 E6 00 86 10 00401130 4C 01 22 00 64 00 AE 01 EA 01 2A 11 E8 10 26 11 00401140 4E 11 8E 11 C2 00 6C 00 0C 11 60 01 CA 00 62 10 00401150 6C 01 A0 11 CE 10 2C 11 4E 10 8C 00 CE 01 AE 01 00401160 6C 10 6C 11 A2 01 AE 00 46 11 EE 10 22 00 A8 00 00401170 EC 01 08 11 A2 01 AE 10 6C 00 6E 00 AC 11 8C 00 00401180 EC 01 2A 10 2A 01 AE 00 40 00 C8 10 48 01 4E 11 00401190 0E 00 EC 11 24 10 4A 10 04 01 C8 11 E6 01 C2 00
2.2. Mapping the real-world problem to an ML problem
2.2.1. Type of Machine Learning Problem
There are nine different classes of malware that we need to classify a given a data point => Multi class classification problem
2.2.2. Performance Metric
Source: https://www.kaggle.com/c/malware-classification#evaluation
Metric(s):
- Multi class log-loss
- Confusion matrix
2.2.3. Machine Learing Objectives and Constraints
Objective: Predict the probability of each data-point belonging to each of the nine classes.
Constraints:
- Class probabilities are needed.
- Penalize the errors in class probabilites => Metric is Log-loss.
- Some Latency constraints.
2.3. Train and Test Dataset
Split the dataset randomly into three parts train, cross validation and test with 64%,16%, 20% of data respectively
2.4. Useful blogs, videos and reference papers
http://blog.kaggle.com/2015/05/26/microsoft-malware-winners-interview-1st-place-no-to-overfitting/
https://arxiv.org/pdf/1511.04317.pdf
First place solution in Kaggle competition: https://www.youtube.com/watch?v=VLQTRlLGz5Y
https://github.com/dchad/malware-detection
http://vizsec.org/files/2011/Nataraj.pdf
https://www.dropbox.com/sh/gfqzv0ckgs4l1bf/AAB6EelnEjvvuQg2nu_pIB6ua?dl=0
" Cross validation is more trustworthy than domain knowledge."
3. Exploratory Data Analysis
In [1]:
import warnings
warnings.filterwarnings("ignore")
import shutil
import os
import pandas as pd
import matplotlib
matplotlib.use(u'nbAgg')
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pickle
from sklearn.manifold import TSNE
from sklearn import preprocessing
import pandas as pd
from multiprocessing import Process# this is used for multithreading
import multiprocessing
import codecs# this is used for file operations
import random as r
from xgboost import XGBClassifier
from sklearn.model_selection import RandomizedSearchCV
from sklearn.tree import DecisionTreeClassifier
from sklearn.calibration import CalibratedClassifierCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import log_loss
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
In [3]:
#separating byte files and asm files
source = 'train'
destination_1 ='byteFiles'
destination_2 = 'asmFiles'
# we will check if the folder 'byteFiles' exists if it not there we will create a folder with the same name
if not os.path.isdir(destination_1):
os.makedirs(destination_1)
if not os.path.isdir(destination_2):
os.makedirs(destination_2)
# if we have folder called 'train' (train folder contains both .asm files and .bytes files) we will rename it 'asmFiles'
# for every file that we have in our 'asmFiles' directory we check if it is ending with .bytes, if yes we will move it to
# 'byteFiles' folder
# so by the end of this snippet we will separate all the .byte files and .asm files
if os.path.isdir(source):
data_files = os.listdir(source)
for file in data_files:
#print(file)
if (file.endswith("bytes")):
shutil.move(source+'\\'+file,destination_1)
if (file.endswith("asm")):
shutil.move(source+'\\'+file,destination_2)
3.1. Distribution of malware classes in whole data set
In [2]:
Y=pd.read_csv(r"C:\Users\hp\Desktop\AppliedAI\ipython notes\microsoftmalware_data\trainLabels.csv")
total = len(Y)*1.
ax=sns.countplot(x="Class", data=Y)
for p in ax.patches:
ax.annotate('{:.1f}%'.format(100*p.get_height()/total), (p.get_x()+0.1, p.get_height()+5))
#put 11 ticks (therefore 10 steps), from 0 to the total number of rows in the dataframe
ax.yaxis.set_ticks(np.linspace(0, total, 11))
#adjust the ticklabel to the desired format, without changing the position of the ticks.
ax.set_yticklabels(map('{:.1f}%'.format, 100*ax.yaxis.get_majorticklocs()/total))
plt.show()
3.2. Feature extraction
3.2.1 File size of byte files as a feature
In [3]:
files=os.listdir('byteFiles')
filenames=Y['Id'].tolist()
class_y=Y['Class'].tolist()
class_bytes=[]
sizebytes=[]
fnames=[]
for file in files:
# print(os.stat('byteFiles/0A32eTdBKayjCWhZqDOQ.txt'))
# os.stat_result(st_mode=33206, st_ino=1125899906874507, st_dev=3561571700, st_nlink=1, st_uid=0, st_gid=0,
# st_size=3680109, st_atime=1519638522, st_mtime=1519638522, st_ctime=1519638522)
# read more about os.stat: here https://www.tutorialspoint.com/python/os_stat.htm
statinfo=os.stat('byteFiles/'+file)
# split the file name at '.' and take the first part of it i.e the file name
file=file.split('.')[0]
if any(file == filename for filename in filenames):
i=filenames.index(file)
class_bytes.append(class_y[i])
# converting into Mb's
sizebytes.append(statinfo.st_size/(1024.0*1024.0))
fnames.append(file)
data_size_byte=pd.DataFrame({'ID':fnames,'size':sizebytes,'Class':class_bytes})
print (data_size_byte.head())
3.2.2 box plots of file size (.byte files) feature
In [4]:
#boxplot of byte files
ax = sns.boxplot(x="Class", y="size", data=data_size_byte)
plt.title("boxplot of .bytes file sizes")
plt.show()
3.2.3 feature extraction from byte files
In [5]:
#removal of addres from byte files
# contents of .byte files
# ----------------
#00401000 56 8D 44 24 08 50 8B F1 E8 1C 1B 00 00 C7 06 08
#-------------------
#we remove the starting address 00401000
files = os.listdir('byteFiles')
filenames=[]
array=[]
for file in files:
if(file.endswith("bytes")):
file=file.split('.')[0]
text_file = open('byteFiles/'+file+".txt", 'w+')
file = file+'.bytes'
with open('byteFiles/'+file,"r") as fp:
lines=""
for line in fp:
a=line.rstrip().split(" ")[1:]
b=' '.join(a)
b=b+"\n"
text_file.write(b)
fp.close()
os.remove('byteFiles/'+file)
text_file.close()
files = os.listdir('byteFiles')
filenames2=[]
feature_matrix = np.zeros((len(files),257),dtype=int)
k=0
In [7]:
#program to convert into bag of words of bytefiles
#this is custom-built bag of words this is unigram bag of words
byte_feature_file=open('result.csv','w+')
byte_feature_file.write("ID,1,2,3,4,5,6,7,8,9,0a,0b,0c,0d,0e,0f,10,11,12,13,14,15,16,17,18,19,1a,1b,1c,1d,1e,1f,20,21,22,23,24,25,26,27,28,29,2a,2b,2c,2d,2e,2f,30,31,32,33,34,35,36,37,38,39,3a,3b,3c,3d,3e,3f,40,41,42,43,44,45,46,47,48,49,4a,4b,4c,4d,4e,4f,50,51,52,53,54,55,56,57,58,59,5a,5b,5c,5d,5e,5f,60,61,62,63,64,65,66,67,68,69,6a,6b,6c,6d,6e,6f,70,71,72,73,74,75,76,77,78,79,7a,7b,7c,7d,7e,7f,80,81,82,83,84,85,86,87,88,89,8a,8b,8c,8d,8e,8f,90,91,92,93,94,95,96,97,98,99,9a,9b,9c,9d,9e,9f,a0,a1,a2,a3,a4,a5,a6,a7,a8,a9,aa,ab,ac,ad,ae,af,b0,b1,b2,b3,b4,b5,b6,b7,b8,b9,ba,bb,bc,bd,be,bf,c0,c1,c2,c3,c4,c5,c6,c7,c8,c9,ca,cb,cc,cd,ce,cf,d0,d1,d2,d3,d4,d5,d6,d7,d8,d9,da,db,dc,dd,de,df,e0,e1,e2,e3,e4,e5,e6,e7,e8,e9,ea,eb,ec,ed,ee,ef,f0,f1,f2,f3,f4,f5,f6,f7,f8,f9,fa,fb,fc,fd,fe,ff,??")
for file in files:
filenames2.append(file)
byte_feature_file.write(file+",")
if(file.endswith("txt")):
with open('byteFiles/'+file,"r") as byte_flie:
for lines in byte_flie:
line=lines.rstrip().split(" ")
for hex_code in line:
if hex_code=='??':
feature_matrix[k][256]+=1
else:
feature_matrix[k][int(hex_code,16)]+=1
byte_flie.close()
for i in feature_matrix[k]:
byte_feature_file.write(str(i)+",")
byte_feature_file.write("\n")
k += 1
byte_feature_file.close()
In [19]:
result = pd.merge(byte_features, data_size_byte,on='ID', how='left')
result.head()
Out[19]:
In [4]:
byte_features=pd.read_csv("result.csv")
print(byte_features.head())
In [5]:
result=byte_features
# https://stackoverflow.com/a/29651514
def normalize(df):
result1 = df.copy()
for feature_name in df.columns:
if (str(feature_name) != str('ID') and str(feature_name)!=str('Class')):
max_value = df[feature_name].max()
min_value = df[feature_name].min()
result1[feature_name] = (df[feature_name] - min_value) / (max_value - min_value)
return result1
result = normalize(result)
3.2.4 Multivariate Analysis
In [11]:
#multivariate analysis on byte files
#this is with perplexity 50
xtsne=TSNE(perplexity=50)
results=xtsne.fit_transform(result.drop(['ID','Class'], axis=1))
vis_x = results[:, 0]
vis_y = results[:, 1]
plt.scatter(vis_x, vis_y, c=data_y, cmap=plt.cm.get_cmap("jet", 9))
plt.colorbar(ticks=range(10))
plt.clim(0.5, 9)
plt.show()
In [15]:
#this is with perplexity 30
xtsne=TSNE(perplexity=30)
results=xtsne.fit_transform(result.drop(['ID','Class'], axis=1))
vis_x = results[:, 0]
vis_y = results[:, 1]
plt.scatter(vis_x, vis_y, c=data_y, cmap=plt.cm.get_cmap("jet", 9))
plt.colorbar(ticks=range(10))
plt.clim(0.5, 9)
plt.show()
Train Test split¶
In [9]:
data_y = result['Class']
# split the data into test and train by maintaining same distribution of output varaible 'y_true' [stratify=y_true]
X_train, X_test, y_train, y_test = train_test_split(result.drop(['ID','Class'], axis=1), data_y,stratify=data_y,test_size=0.20)
# split the train data into train and cross validation by maintaining same distribution of output varaible 'y_train' [stratify=y_train]
X_train, X_cv, y_train, y_cv = train_test_split(X_train, y_train,stratify=y_train,test_size=0.20)
In [10]:
print('Number of data points in train data:', X_train.shape[0])
print('Number of data points in test data:', X_test.shape[0])
print('Number of data points in cross validation data:', X_cv.shape[0])
In [11]:
# it returns a dict, keys as class labels and values as the number of data points in that class
train_class_distribution = y_train.value_counts().sortlevel()
test_class_distribution = y_test.value_counts().sortlevel()
cv_class_distribution = y_cv.value_counts().sortlevel()
my_colors = ['#b23850', '#3b8beb', '#e7e3d4', '#c4dbf6', '#8590aa', '#0d19a3', '#15db95', '#080f5b', '#f79e02']
train_class_distribution.plot(kind='bar', color=my_colors)
plt.xlabel('Class')
plt.ylabel('Data points per Class')
plt.title('Distribution of yi in train data')
plt.grid()
plt.show()
# ref: argsort https://docs.scipy.org/doc/numpy/reference/generated/numpy.argsort.html
# -(train_class_distribution.values): the minus sign will give us in decreasing order
sorted_yi = np.argsort(-train_class_distribution.values)
for i in sorted_yi:
print('Number of data points in class', i+1, ':',train_class_distribution.values[i], '(', np.round((train_class_distribution.values[i]/y_train.shape[0]*100), 3), '%)')
print('-'*80)
my_colors = ['#b23850', '#3b8beb', '#e7e3d4', '#c4dbf6', '#8590aa', '#0d19a3', '#15db95', '#080f5b', '#f79e02']
test_class_distribution.plot(kind='bar', color=my_colors)
plt.xlabel('Class')
plt.ylabel('Data points per Class')
plt.title('Distribution of yi in test data')
plt.grid()
plt.show()
# ref: argsort https://docs.scipy.org/doc/numpy/reference/generated/numpy.argsort.html
# -(train_class_distribution.values): the minus sign will give us in decreasing order
sorted_yi = np.argsort(-test_class_distribution.values)
for i in sorted_yi:
print('Number of data points in class', i+1, ':',test_class_distribution.values[i], '(', np.round((test_class_distribution.values[i]/y_test.shape[0]*100), 3), '%)')
print('-'*80)
my_colors = ['#b23850', '#3b8beb', '#e7e3d4', '#c4dbf6', '#8590aa', '#0d19a3', '#15db95', '#080f5b', '#f79e02']
cv_class_distribution.plot(kind='bar', color=my_colors)
plt.xlabel('Class')
plt.ylabel('Data points per Class')
plt.title('Distribution of yi in cross validation data')
plt.grid()
plt.show()
# ref: argsort https://docs.scipy.org/doc/numpy/reference/generated/numpy.argsort.html
# -(train_class_distribution.values): the minus sign will give us in decreasing order
sorted_yi = np.argsort(-train_class_distribution.values)
for i in sorted_yi:
print('Number of data points in class', i+1, ':',cv_class_distribution.values[i], '(', np.round((cv_class_distribution.values[i]/y_cv.shape[0]*100), 3), '%)')
In [13]:
def plot_confusion_matrix(test_y, predict_y):
C = confusion_matrix(test_y, predict_y)
print("Number of misclassified points ",(len(test_y)-np.trace(C))/len(test_y)*100)
# C = 9,9 matrix, each cell (i,j) represents number of points of class i are predicted class j
A =(((C.T)/(C.sum(axis=1))).T)
#divid each element of the confusion matrix with the sum of elements in that column
# C = [[1, 2],
# [3, 4]]
# C.T = [[1, 3],
# [2, 4]]
# C.sum(axis = 1) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array
# C.sum(axix =1) = [[3, 7]]
# ((C.T)/(C.sum(axis=1))) = [[1/3, 3/7]
# [2/3, 4/7]]
# ((C.T)/(C.sum(axis=1))).T = [[1/3, 2/3]
# [3/7, 4/7]]
# sum of row elements = 1
B =(C/C.sum(axis=0))
#divid each element of the confusion matrix with the sum of elements in that row
# C = [[1, 2],
# [3, 4]]
# C.sum(axis = 0) axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array
# C.sum(axix =0) = [[4, 6]]
# (C/C.sum(axis=0)) = [[1/4, 2/6],
# [3/4, 4/6]]
labels = [1,2,3,4,5,6,7,8,9]
cmap=sns.light_palette("green")
# representing A in heatmap format
print("-"*50, "Confusion matrix", "-"*50)
plt.figure(figsize=(10,5))
sns.heatmap(C, annot=True, cmap=cmap, fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
print("-"*50, "Precision matrix", "-"*50)
plt.figure(figsize=(10,5))
sns.heatmap(B, annot=True, cmap=cmap, fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
print("Sum of columns in precision matrix",B.sum(axis=0))
# representing B in heatmap format
print("-"*50, "Recall matrix" , "-"*50)
plt.figure(figsize=(10,5))
sns.heatmap(A, annot=True, cmap=cmap, fmt=".3f", xticklabels=labels, yticklabels=labels)
plt.xlabel('Predicted Class')
plt.ylabel('Original Class')
plt.show()
print("Sum of rows in precision matrix",A.sum(axis=1))
4. Machine Learning Models
4.1. Machine Leaning Models on bytes files
4.1.1. Random Model
In [20]:
# we need to generate 9 numbers and the sum of numbers should be 1
# one solution is to genarate 9 numbers and divide each of the numbers by their sum
# ref: https://stackoverflow.com/a/18662466/4084039
test_data_len = X_test.shape[0]
cv_data_len = X_cv.shape[0]
# we create a output array that has exactly same size as the CV data
cv_predicted_y = np.zeros((cv_data_len,9))
for i in range(cv_data_len):
rand_probs = np.random.rand(1,9)
cv_predicted_y[i] = ((rand_probs/sum(sum(rand_probs)))[0])
print("Log loss on Cross Validation Data using Random Model",log_loss(y_cv,cv_predicted_y, eps=1e-15))
# Test-Set error.
#we create a output array that has exactly same as the test data
test_predicted_y = np.zeros((test_data_len,9))
for i in range(test_data_len):
rand_probs = np.random.rand(1,9)
test_predicted_y[i] = ((rand_probs/sum(sum(rand_probs)))[0])
print("Log loss on Test Data using Random Model",log_loss(y_test,test_predicted_y, eps=1e-15))
predicted_y =np.argmax(test_predicted_y, axis=1)
plot_confusion_matrix(y_test, predicted_y+1)
4.1.2. K Nearest Neighbour Classification
In [21]:
# find more about KNeighborsClassifier() here http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html
# -------------------------
# default parameter
# KNeighborsClassifier(n_neighbors=5, weights=’uniform’, algorithm=’auto’, leaf_size=30, p=2,
# metric=’minkowski’, metric_params=None, n_jobs=1, **kwargs)
# methods of
# fit(X, y) : Fit the model using X as training data and y as target values
# predict(X):Predict the class labels for the provided data
# predict_proba(X):Return probability estimates for the test data X.
#-------------------------------------
# video link: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/k-nearest-neighbors-geometric-intuition-with-a-toy-example-1/
#-------------------------------------
# find more about CalibratedClassifierCV here at http://scikit-learn.org/stable/modules/generated/sklearn.calibration.CalibratedClassifierCV.html
# ----------------------------
# default paramters
# sklearn.calibration.CalibratedClassifierCV(base_estimator=None, method=’sigmoid’, cv=3)
#
# some of the methods of CalibratedClassifierCV()
# fit(X, y[, sample_weight]) Fit the calibrated model
# get_params([deep]) Get parameters for this estimator.
# predict(X) Predict the target of new samples.
# predict_proba(X) Posterior probabilities of classification
#-------------------------------------
# video link:
#-------------------------------------
alpha = [x for x in range(1, 15, 2)]
cv_log_error_array=[]
for i in alpha:
k_cfl=KNeighborsClassifier(n_neighbors=i)
k_cfl.fit(X_train,y_train)
sig_clf = CalibratedClassifierCV(k_cfl, method="sigmoid")
sig_clf.fit(X_train, y_train)
predict_y = sig_clf.predict_proba(X_cv)
cv_log_error_array.append(log_loss(y_cv, predict_y, labels=k_cfl.classes_, eps=1e-15))
for i in range(len(cv_log_error_array)):
print ('log_loss for k = ',alpha[i],'is',cv_log_error_array[i])
best_alpha = np.argmin(cv_log_error_array)
fig, ax = plt.subplots()
ax.plot(alpha, cv_log_error_array,c='g')
for i, txt in enumerate(np.round(cv_log_error_array,3)):
ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))
plt.grid()
plt.title("Cross Validation Error for each alpha")
plt.xlabel("Alpha i's")
plt.ylabel("Error measure")
plt.show()
k_cfl=KNeighborsClassifier(n_neighbors=alpha[best_alpha])
k_cfl.fit(X_train,y_train)
sig_clf = CalibratedClassifierCV(k_cfl, method="sigmoid")
sig_clf.fit(X_train, y_train)
predict_y = sig_clf.predict_proba(X_train)
print ('For values of best alpha = ', alpha[best_alpha], "The train log loss is:",log_loss(y_train, predict_y))
predict_y = sig_clf.predict_proba(X_cv)
print('For values of best alpha = ', alpha[best_alpha], "The cross validation log loss is:",log_loss(y_cv, predict_y))
predict_y = sig_clf.predict_proba(X_test)
print('For values of best alpha = ', alpha[best_alpha], "The test log loss is:",log_loss(y_test, predict_y))
plot_confusion_matrix(y_test, sig_clf.predict(X_test))
4.1.3. Logistic Regression
In [22]:
# read more about SGDClassifier() at http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html
# ------------------------------
# default parameters
# SGDClassifier(loss=’hinge’, penalty=’l2’, alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=None, tol=None,
# shuffle=True, verbose=0, epsilon=0.1, n_jobs=1, random_state=None, learning_rate=’optimal’, eta0=0.0, power_t=0.5,
# class_weight=None, warm_start=False, average=False, n_iter=None)
# some of methods
# fit(X, y[, coef_init, intercept_init, …]) Fit linear model with Stochastic Gradient Descent.
# predict(X) Predict class labels for samples in X.
#-------------------------------
# video link: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/geometric-intuition-1/
#------------------------------
alpha = [10 ** x for x in range(-5, 4)]
cv_log_error_array=[]
for i in alpha:
logisticR=LogisticRegression(penalty='l2',C=i,class_weight='balanced')
logisticR.fit(X_train,y_train)
sig_clf = CalibratedClassifierCV(logisticR, method="sigmoid")
sig_clf.fit(X_train, y_train)
predict_y = sig_clf.predict_proba(X_cv)
cv_log_error_array.append(log_loss(y_cv, predict_y, labels=logisticR.classes_, eps=1e-15))
for i in range(len(cv_log_error_array)):
print ('log_loss for c = ',alpha[i],'is',cv_log_error_array[i])
best_alpha = np.argmin(cv_log_error_array)
fig, ax = plt.subplots()
ax.plot(alpha, cv_log_error_array,c='g')
for i, txt in enumerate(np.round(cv_log_error_array,3)):
ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))
plt.grid()
plt.title("Cross Validation Error for each alpha")
plt.xlabel("Alpha i's")
plt.ylabel("Error measure")
plt.show()
logisticR=LogisticRegression(penalty='l2',C=alpha[best_alpha],class_weight='balanced')
logisticR.fit(X_train,y_train)
sig_clf = CalibratedClassifierCV(logisticR, method="sigmoid")
sig_clf.fit(X_train, y_train)
pred_y=sig_clf.predict(X_test)
predict_y = sig_clf.predict_proba(X_train)
print ('log loss for train data',log_loss(y_train, predict_y, labels=logisticR.classes_, eps=1e-15))
predict_y = sig_clf.predict_proba(X_cv)
print ('log loss for cv data',log_loss(y_cv, predict_y, labels=logisticR.classes_, eps=1e-15))
predict_y = sig_clf.predict_proba(X_test)
print ('log loss for test data',log_loss(y_test, predict_y, labels=logisticR.classes_, eps=1e-15))
plot_confusion_matrix(y_test, sig_clf.predict(X_test))
4.1.4. Random Forest Classifier
In [23]:
# --------------------------------
# default parameters
# sklearn.ensemble.RandomForestClassifier(n_estimators=10, criterion=’gini’, max_depth=None, min_samples_split=2,
# min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=’auto’, max_leaf_nodes=None, min_impurity_decrease=0.0,
# min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False,
# class_weight=None)
# Some of methods of RandomForestClassifier()
# fit(X, y, [sample_weight]) Fit the SVM model according to the given training data.
# predict(X) Perform classification on samples in X.
# predict_proba (X) Perform classification on samples in X.
# some of attributes of RandomForestClassifier()
# feature_importances_ : array of shape = [n_features]
# The feature importances (the higher, the more important the feature).
# --------------------------------
# video link: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/random-forest-and-their-construction-2/
# --------------------------------
alpha=[10,50,100,500,1000,2000,3000]
cv_log_error_array=[]
train_log_error_array=[]
from sklearn.ensemble import RandomForestClassifier
for i in alpha:
r_cfl=RandomForestClassifier(n_estimators=i,random_state=42,n_jobs=-1)
r_cfl.fit(X_train,y_train)
sig_clf = CalibratedClassifierCV(r_cfl, method="sigmoid")
sig_clf.fit(X_train, y_train)
predict_y = sig_clf.predict_proba(X_cv)
cv_log_error_array.append(log_loss(y_cv, predict_y, labels=r_cfl.classes_, eps=1e-15))
for i in range(len(cv_log_error_array)):
print ('log_loss for c = ',alpha[i],'is',cv_log_error_array[i])
best_alpha = np.argmin(cv_log_error_array)
fig, ax = plt.subplots()
ax.plot(alpha, cv_log_error_array,c='g')
for i, txt in enumerate(np.round(cv_log_error_array,3)):
ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))
plt.grid()
plt.title("Cross Validation Error for each alpha")
plt.xlabel("Alpha i's")
plt.ylabel("Error measure")
plt.show()
r_cfl=RandomForestClassifier(n_estimators=alpha[best_alpha],random_state=42,n_jobs=-1)
r_cfl.fit(X_train,y_train)
sig_clf = CalibratedClassifierCV(r_cfl, method="sigmoid")
sig_clf.fit(X_train, y_train)
predict_y = sig_clf.predict_proba(X_train)
print('For values of best alpha = ', alpha[best_alpha], "The train log loss is:",log_loss(y_train, predict_y))
predict_y = sig_clf.predict_proba(X_cv)
print('For values of best alpha = ', alpha[best_alpha], "The cross validation log loss is:",log_loss(y_cv, predict_y))
predict_y = sig_clf.predict_proba(X_test)
print('For values of best alpha = ', alpha[best_alpha], "The test log loss is:",log_loss(y_test, predict_y))
plot_confusion_matrix(y_test, sig_clf.predict(X_test))
4.1.5. XgBoost Classification
In [14]:
# Training a hyper-parameter tuned Xg-Boost regressor on our train data
# find more about XGBClassifier function here http://xgboost.readthedocs.io/en/latest/python/python_api.html?#xgboost.XGBClassifier
# -------------------------
# default paramters
# class xgboost.XGBClassifier(max_depth=3, learning_rate=0.1, n_estimators=100, silent=True,
# objective='binary:logistic', booster='gbtree', n_jobs=1, nthread=None, gamma=0, min_child_weight=1,
# max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1,
# scale_pos_weight=1, base_score=0.5, random_state=0, seed=None, missing=None, **kwargs)
# some of methods of RandomForestRegressor()
# fit(X, y, sample_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None)
# get_params([deep]) Get parameters for this estimator.
# predict(data, output_margin=False, ntree_limit=0) : Predict with data. NOTE: This function is not thread safe.
# get_score(importance_type='weight') -> get the feature importance
# -----------------------
# video link1: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/regression-using-decision-trees-2/
# video link2: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/what-are-ensembles/
# -----------------------
alpha=[10,50,100,500,1000,2000]
cv_log_error_array=[]
for i in alpha:
x_cfl=XGBClassifier(n_estimators=i,nthread=-1)
x_cfl.fit(X_train,y_train)
sig_clf = CalibratedClassifierCV(x_cfl, method="sigmoid")
sig_clf.fit(X_train, y_train)
predict_y = sig_clf.predict_proba(X_cv)
cv_log_error_array.append(log_loss(y_cv, predict_y, labels=x_cfl.classes_, eps=1e-15))
for i in range(len(cv_log_error_array)):
print ('log_loss for c = ',alpha[i],'is',cv_log_error_array[i])
best_alpha = np.argmin(cv_log_error_array)
fig, ax = plt.subplots()
ax.plot(alpha, cv_log_error_array,c='g')
for i, txt in enumerate(np.round(cv_log_error_array,3)):
ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))
plt.grid()
plt.title("Cross Validation Error for each alpha")
plt.xlabel("Alpha i's")
plt.ylabel("Error measure")
plt.show()
x_cfl=XGBClassifier(n_estimators=alpha[best_alpha],nthread=-1)
x_cfl.fit(X_train,y_train)
sig_clf = CalibratedClassifierCV(x_cfl, method="sigmoid")
sig_clf.fit(X_train, y_train)
predict_y = sig_clf.predict_proba(X_train)
print ('For values of best alpha = ', alpha[best_alpha], "The train log loss is:",log_loss(y_train, predict_y))
predict_y = sig_clf.predict_proba(X_cv)
print('For values of best alpha = ', alpha[best_alpha], "The cross validation log loss is:",log_loss(y_cv, predict_y))
predict_y = sig_clf.predict_proba(X_test)
print('For values of best alpha = ', alpha[best_alpha], "The test log loss is:",log_loss(y_test, predict_y))
In [15]:
plot_confusion_matrix(y_test, sig_clf.predict(X_test))
4.1.5. XgBoost Classification with best hyper parameters using RandomSearch
In [22]:
# https://www.analyticsvidhya.com/blog/2016/03/complete-guide-parameter-tuning-xgboost-with-codes-python/
x_cfl=XGBClassifier()
prams={
'learning_rate':[0.01,0.03,0.05,0.1,0.15,0.2],
'n_estimators':[100,200,500,1000,2000],
'max_depth':[3,5,10],
'colsample_bytree':[0.1,0.3,0.5,1],
'subsample':[0.1,0.3,0.5,1]
}
random_cfl1=RandomizedSearchCV(x_cfl,param_distributions=prams,verbose=10,n_jobs=-1,)
random_cfl1.fit(X_train,y_train)
Out[22]:
In [ ]:
print (random_cfl1.best_params_)
In [16]:
x_cfl=XGBClassifier(n_estimators=2000, learning_rate=0.01, colsample_bytree=0.5, max_depth=5,subsample=1)
x_cfl.fit(X_train,y_train)
c_cfl=CalibratedClassifierCV(x_cfl,method='sigmoid')
c_cfl.fit(X_train,y_train)
predict_y = c_cfl.predict_proba(X_train)
print ('train loss',log_loss(y_train, predict_y))
predict_y = c_cfl.predict_proba(X_cv)
print ('cv loss',log_loss(y_cv, predict_y))
predict_y = c_cfl.predict_proba(X_test)
print ('test loss',log_loss(y_test, predict_y))
4.2 Modeling with .asm files
There are 10868 files of asm All the files make up about 150 GB The asm files contains : 1. Address 2. Segments 3. Opcodes 4. Registers 5. function calls 6. APIs With the help of parallel processing we extracted all the features.In parallel we can use all the cores that are present in our computer. Here we extracted 52 features from all the asm files which are important. We read the top solutions and handpicked the features from those papers/videos/blogs.
Refer:https://www.kaggle.com/c/malware-classification/discussion
4.2.1 Feature extraction from asm files
In [ ]:
#intially create five folders
#first
#second
#thrid
#fourth
#fifth
#this code tells us about random split of files into five folders
folder_1 ='first'
folder_2 ='second'
folder_3 ='third'
folder_4 ='fourth'
folder_5 ='fifth'
folder_6 = 'output'
for i in [folder_1,folder_2,folder_3,folder_4,folder_5,folder_6]:
if not os.path.isdir(i):
os.makedirs(i)
source='train/'
files = os.listdir('train')
ID=df['Id'].tolist()
data=range(0,10868)
r.shuffle(data)
count=0
for i in range(0,10868):
if i % 5==0:
shutil.move(source+files[data[i]],'first')
elif i%5==1:
shutil.move(source+files[data[i]],'second')
elif i%5 ==2:
shutil.move(source+files[data[i]],'thrid')
elif i%5 ==3:
shutil.move(source+files[data[i]],'fourth')
elif i%5==4:
shutil.move(source+files[data[i]],'fifth')
In [ ]:
#http://flint.cs.yale.edu/cs421/papers/x86-asm/asm.html
def firstprocess():
#The prefixes tells about the segments that are present in the asm files
#There are 450 segments(approx) present in all asm files.
#this prefixes are best segments that gives us best values.
#https://en.wikipedia.org/wiki/Data_segment
prefixes = ['HEADER:','.text:','.Pav:','.idata:','.data:','.bss:','.rdata:','.edata:','.rsrc:','.tls:','.reloc:','.BSS:','.CODE']
#this are opcodes that are used to get best results
#https://en.wikipedia.org/wiki/X86_instruction_listings
opcodes = ['jmp', 'mov', 'retf', 'push', 'pop', 'xor', 'retn', 'nop', 'sub', 'inc', 'dec', 'add','imul', 'xchg', 'or', 'shr', 'cmp', 'call', 'shl', 'ror', 'rol', 'jnb','jz','rtn','lea','movzx']
#best keywords that are taken from different blogs
keywords = ['.dll','std::',':dword']
#Below taken registers are general purpose registers and special registers
#All the registers which are taken are best
registers=['edx','esi','eax','ebx','ecx','edi','ebp','esp','eip']
file1=open("output\asmsmallfile.txt","w+")
files = os.listdir('first')
for f in files:
#filling the values with zeros into the arrays
prefixescount=np.zeros(len(prefixes),dtype=int)
opcodescount=np.zeros(len(opcodes),dtype=int)
keywordcount=np.zeros(len(keywords),dtype=int)
registerscount=np.zeros(len(registers),dtype=int)
features=[]
f2=f.split('.')[0]
file1.write(f2+",")
opcodefile.write(f2+" ")
# https://docs.python.org/3/library/codecs.html#codecs.ignore_errors
# https://docs.python.org/3/library/codecs.html#codecs.Codec.encode
with codecs.open('first/'+f,encoding='cp1252',errors ='replace') as fli:
for lines in fli:
# https://www.tutorialspoint.com/python3/string_rstrip.htm
line=lines.rstrip().split()
l=line[0]
#counting the prefixs in each and every line
for i in range(len(prefixes)):
if prefixes[i] in line[0]:
prefixescount[i]+=1
line=line[1:]
#counting the opcodes in each and every line
for i in range(len(opcodes)):
if any(opcodes[i]==li for li in line):
features.append(opcodes[i])
opcodescount[i]+=1
#counting registers in the line
for i in range(len(registers)):
for li in line:
# we will use registers only in 'text' and 'CODE' segments
if registers[i] in li and ('text' in l or 'CODE' in l):
registerscount[i]+=1
#counting keywords in the line
for i in range(len(keywords)):
for li in line:
if keywords[i] in li:
keywordcount[i]+=1
#pushing the values into the file after reading whole file
for prefix in prefixescount:
file1.write(str(prefix)+",")
for opcode in opcodescount:
file1.write(str(opcode)+",")
for register in registerscount:
file1.write(str(register)+",")
for key in keywordcount:
file1.write(str(key)+",")
file1.write("\n")
file1.close()
#same as above
def secondprocess():
prefixes = ['HEADER:','.text:','.Pav:','.idata:','.data:','.bss:','.rdata:','.edata:','.rsrc:','.tls:','.reloc:','.BSS:','.CODE']
opcodes = ['jmp', 'mov', 'retf', 'push', 'pop', 'xor', 'retn', 'nop', 'sub', 'inc', 'dec', 'add','imul', 'xchg', 'or', 'shr', 'cmp', 'call', 'shl', 'ror', 'rol', 'jnb','jz','rtn','lea','movzx']
keywords = ['.dll','std::',':dword']
registers=['edx','esi','eax','ebx','ecx','edi','ebp','esp','eip']
file1=open("output\mediumasmfile.txt","w+")
files = os.listdir('second')
for f in files:
prefixescount=np.zeros(len(prefixes),dtype=int)
opcodescount=np.zeros(len(opcodes),dtype=int)
keywordcount=np.zeros(len(keywords),dtype=int)
registerscount=np.zeros(len(registers),dtype=int)
features=[]
f2=f.split('.')[0]
file1.write(f2+",")
opcodefile.write(f2+" ")
with codecs.open('second/'+f,encoding='cp1252',errors ='replace') as fli:
for lines in fli:
line=lines.rstrip().split()
l=line[0]
for i in range(len(prefixes)):
if prefixes[i] in line[0]:
prefixescount[i]+=1
line=line[1:]
for i in range(len(opcodes)):
if any(opcodes[i]==li for li in line):
features.append(opcodes[i])
opcodescount[i]+=1
for i in range(len(registers)):
for li in line:
if registers[i] in li and ('text' in l or 'CODE' in l):
registerscount[i]+=1
for i in range(len(keywords)):
for li in line:
if keywords[i] in li:
keywordcount[i]+=1
for prefix in prefixescount:
file1.write(str(prefix)+",")
for opcode in opcodescount:
file1.write(str(opcode)+",")
for register in registerscount:
file1.write(str(register)+",")
for key in keywordcount:
file1.write(str(key)+",")
file1.write("\n")
file1.close()
# same as smallprocess() functions
def thirdprocess():
prefixes = ['HEADER:','.text:','.Pav:','.idata:','.data:','.bss:','.rdata:','.edata:','.rsrc:','.tls:','.reloc:','.BSS:','.CODE']
opcodes = ['jmp', 'mov', 'retf', 'push', 'pop', 'xor', 'retn', 'nop', 'sub', 'inc', 'dec', 'add','imul', 'xchg', 'or', 'shr', 'cmp', 'call', 'shl', 'ror', 'rol', 'jnb','jz','rtn','lea','movzx']
keywords = ['.dll','std::',':dword']
registers=['edx','esi','eax','ebx','ecx','edi','ebp','esp','eip']
file1=open("output\largeasmfile.txt","w+")
files = os.listdir('thrid')
for f in files:
prefixescount=np.zeros(len(prefixes),dtype=int)
opcodescount=np.zeros(len(opcodes),dtype=int)
keywordcount=np.zeros(len(keywords),dtype=int)
registerscount=np.zeros(len(registers),dtype=int)
features=[]
f2=f.split('.')[0]
file1.write(f2+",")
opcodefile.write(f2+" ")
with codecs.open('thrid/'+f,encoding='cp1252',errors ='replace') as fli:
for lines in fli:
line=lines.rstrip().split()
l=line[0]
for i in range(len(prefixes)):
if prefixes[i] in line[0]:
prefixescount[i]+=1
line=line[1:]
for i in range(len(opcodes)):
if any(opcodes[i]==li for li in line):
features.append(opcodes[i])
opcodescount[i]+=1
for i in range(len(registers)):
for li in line:
if registers[i] in li and ('text' in l or 'CODE' in l):
registerscount[i]+=1
for i in range(len(keywords)):
for li in line:
if keywords[i] in li:
keywordcount[i]+=1
for prefix in prefixescount:
file1.write(str(prefix)+",")
for opcode in opcodescount:
file1.write(str(opcode)+",")
for register in registerscount:
file1.write(str(register)+",")
for key in keywordcount:
file1.write(str(key)+",")
file1.write("\n")
file1.close()
def fourthprocess():
prefixes = ['HEADER:','.text:','.Pav:','.idata:','.data:','.bss:','.rdata:','.edata:','.rsrc:','.tls:','.reloc:','.BSS:','.CODE']
opcodes = ['jmp', 'mov', 'retf', 'push', 'pop', 'xor', 'retn', 'nop', 'sub', 'inc', 'dec', 'add','imul', 'xchg', 'or', 'shr', 'cmp', 'call', 'shl', 'ror', 'rol', 'jnb','jz','rtn','lea','movzx']
keywords = ['.dll','std::',':dword']
registers=['edx','esi','eax','ebx','ecx','edi','ebp','esp','eip']
file1=open("output\hugeasmfile.txt","w+")
files = os.listdir('fourth/')
for f in files:
prefixescount=np.zeros(len(prefixes),dtype=int)
opcodescount=np.zeros(len(opcodes),dtype=int)
keywordcount=np.zeros(len(keywords),dtype=int)
registerscount=np.zeros(len(registers),dtype=int)
features=[]
f2=f.split('.')[0]
file1.write(f2+",")
opcodefile.write(f2+" ")
with codecs.open('fourth/'+f,encoding='cp1252',errors ='replace') as fli:
for lines in fli:
line=lines.rstrip().split()
l=line[0]
for i in range(len(prefixes)):
if prefixes[i] in line[0]:
prefixescount[i]+=1
line=line[1:]
for i in range(len(opcodes)):
if any(opcodes[i]==li for li in line):
features.append(opcodes[i])
opcodescount[i]+=1
for i in range(len(registers)):
for li in line:
if registers[i] in li and ('text' in l or 'CODE' in l):
registerscount[i]+=1
for i in range(len(keywords)):
for li in line:
if keywords[i] in li:
keywordcount[i]+=1
for prefix in prefixescount:
file1.write(str(prefix)+",")
for opcode in opcodescount:
file1.write(str(opcode)+",")
for register in registerscount:
file1.write(str(register)+",")
for key in keywordcount:
file1.write(str(key)+",")
file1.write("\n")
file1.close()
def fifthprocess():
prefixes = ['HEADER:','.text:','.Pav:','.idata:','.data:','.bss:','.rdata:','.edata:','.rsrc:','.tls:','.reloc:','.BSS:','.CODE']
opcodes = ['jmp', 'mov', 'retf', 'push', 'pop', 'xor', 'retn', 'nop', 'sub', 'inc', 'dec', 'add','imul', 'xchg', 'or', 'shr', 'cmp', 'call', 'shl', 'ror', 'rol', 'jnb','jz','rtn','lea','movzx']
keywords = ['.dll','std::',':dword']
registers=['edx','esi','eax','ebx','ecx','edi','ebp','esp','eip']
file1=open("output\trainasmfile.txt","w+")
files = os.listdir('fifth/')
for f in files:
prefixescount=np.zeros(len(prefixes),dtype=int)
opcodescount=np.zeros(len(opcodes),dtype=int)
keywordcount=np.zeros(len(keywords),dtype=int)
registerscount=np.zeros(len(registers),dtype=int)
features=[]
f2=f.split('.')[0]
file1.write(f2+",")
opcodefile.write(f2+" ")
with codecs.open('fifth/'+f,encoding='cp1252',errors ='replace') as fli:
for lines in fli:
line=lines.rstrip().split()
l=line[0]
for i in range(len(prefixes)):
if prefixes[i] in line[0]:
prefixescount[i]+=1
line=line[1:]
for i in range(len(opcodes)):
if any(opcodes[i]==li for li in line):
features.append(opcodes[i])
opcodescount[i]+=1
for i in range(len(registers)):
for li in line:
if registers[i] in li and ('text' in l or 'CODE' in l):
registerscount[i]+=1
for i in range(len(keywords)):
for li in line:
if keywords[i] in li:
keywordcount[i]+=1
for prefix in prefixescount:
file1.write(str(prefix)+",")
for opcode in opcodescount:
file1.write(str(opcode)+",")
for register in registerscount:
file1.write(str(register)+",")
for key in keywordcount:
file1.write(str(key)+",")
file1.write("\n")
file1.close()
def main():
#the below code is used for multiprogramming
#the number of process depends upon the number of cores present System
#process is used to call multiprogramming
manager=multiprocessing.Manager()
p1=Process(target=firstprocess)
p2=Process(target=secondprocess)
p3=Process(target=thirdprocess)
p4=Process(target=fourthprocess)
p5=Process(target=fifthprocess)
#p1.start() is used to start the thread execution
p1.start()
p2.start()
p3.start()
p4.start()
p5.start()
#After completion all the threads are joined
p1.join()
p2.join()
p3.join()
p4.join()
p5.join()
if __name__=="__main__":
main()
In [14]:
# asmoutputfile.csv(output genarated from the above two cells) will contain all the extracted features from .asm files
# this file will be uploaded in the drive, you can directly use this
dfasm=pd.read_csv("asmoutputfile (1).csv")
Y.columns = ['ID', 'Class']
result_asm = pd.merge(dfasm, Y,on='ID', how='left')
result_asm.head()
Out[14]:
4.2.1.1 Files sizes of each .asm file
In [15]:
#file sizes of byte files
files=os.listdir('asmFiles')
filenames=Y['ID'].tolist()
class_y=Y['Class'].tolist()
class_bytes=[]
sizebytes=[]
fnames=[]
for file in files:
# print(os.stat('byteFiles/0A32eTdBKayjCWhZqDOQ.txt'))
# os.stat_result(st_mode=33206, st_ino=1125899906874507, st_dev=3561571700, st_nlink=1, st_uid=0, st_gid=0,
# st_size=3680109, st_atime=1519638522, st_mtime=1519638522, st_ctime=1519638522)
# read more about os.stat: here https://www.tutorialspoint.com/python/os_stat.htm
statinfo=os.stat('asmFiles/'+file)
# split the file name at '.' and take the first part of it i.e the file name
file=file.split('.')[0]
if any(file == filename for filename in filenames):
i=filenames.index(file)
class_bytes.append(class_y[i])
# converting into Mb's
sizebytes.append(statinfo.st_size/(1024.0*1024.0))
fnames.append(file)
asm_size_byte=pd.DataFrame({'ID':fnames,'size':sizebytes,'Class':class_bytes})
print (asm_size_byte.head())
4.2.1.2 Distribution of .asm file sizes
In [15]:
#boxplot of asm files
ax = sns.boxplot(x="Class", y="size", data=asm_size_byte)
plt.title("boxplot of .bytes file sizes")
plt.show()
In [16]:
# add the file size feature to previous extracted features
print(result_asm.shape)
print(asm_size_byte.shape)
result_asm = pd.merge(result_asm, asm_size_byte.drop(['Class'], axis=1),on='ID', how='left')
result_asm.head()
Out[16]:
In [17]:
# we normalize the data each column
result_asm.head()
Out[17]:
4.2.2 Univariate analysis on asm file features
In [18]:
ax = sns.boxplot(x="Class", y=".text:", data=result_asm)
plt.title("boxplot of .asm text segment")
plt.show()
The plot is between Text and class Class 1,2 and 9 can be easly separated
In [19]:
ax = sns.boxplot(x="Class", y=".Pav:", data=result_asm)
plt.title("boxplot of .asm pav segment")
plt.show()
In [19]:
ax = sns.boxplot(x="Class", y=".data:", data=result_asm)
plt.title("boxplot of .asm data segment")
plt.show()
The plot is between data segment and class label class 6 and class 9 can be easily separated from given points
In [20]:
ax = sns.boxplot(x="Class", y=".bss:", data=result_asm)
plt.title("boxplot of .asm bss segment")
plt.show()
plot between bss segment and class label very less number of files are having bss segment
In [21]:
ax = sns.boxplot(x="Class", y=".rdata:", data=result_asm)
plt.title("boxplot of .asm rdata segment")
plt.show()
Plot between rdata segment and Class segment Class 2 can be easily separated 75 pecentile files are having 1M rdata lines
In [22]:
ax = sns.boxplot(x="Class", y="jmp", data=result_asm)
plt.title("boxplot of .asm jmp opcode")
plt.show()
plot between jmp and Class label Class 1 is having frequency of 2000 approx in 75 perentile of files
In [23]:
ax = sns.boxplot(x="Class", y="mov", data=result_asm)
plt.title("boxplot of .asm mov opcode")
plt.show()
plot between Class label and mov opcode Class 1 is having frequency of 2000 approx in 75 perentile of files
In [24]:
ax = sns.boxplot(x="Class", y="retf", data=result_asm)
plt.title("boxplot of .asm retf opcode")
plt.show()
plot between Class label and retf Class 6 can be easily separated with opcode retf The frequency of retf is approx of 250.
In [25]:
ax = sns.boxplot(x="Class", y="push", data=result_asm)
plt.title("boxplot of .asm push opcode")
plt.show()
plot between push opcode and Class label Class 1 is having 75 precentile files with push opcodes of frequency 1000
4.2.2 Multivariate Analysis on .asm file features
In [16]:
# check out the course content for more explantion on tsne algorithm
# https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/t-distributed-stochastic-neighbourhood-embeddingt-sne-part-1/
#multivariate analysis on byte files
#this is with perplexity 50
xtsne=TSNE(perplexity=50)
results=xtsne.fit_transform(result_asm.drop(['ID','Class'], axis=1).fillna(0))
vis_x = results[:, 0]
vis_y = results[:, 1 ]
plt.scatter(vis_x, vis_y, c=data_y, cmap=plt.cm.get_cmap("jet", 9))
plt.colorbar(ticks=range(10))
plt.clim(0.5, 9)
plt.show()
In [30]:
# by univariate analysis on the .asm file features we are getting very negligible information from
# 'rtn', '.BSS:' '.CODE' features, so heare we are trying multivariate analysis after removing those features
# the plot looks very messy
xtsne=TSNE(perplexity=30)
results=xtsne.fit_transform(result_asm.drop(['ID','Class', 'rtn', '.BSS:', '.CODE','size'], axis=1))
vis_x = results[:, 0]
vis_y = results[:, 1]
plt.scatter(vis_x, vis_y, c=data_y, cmap=plt.cm.get_cmap("jet", 9))
plt.colorbar(ticks=range(10))
plt.clim(0.5, 9)
plt.show()
TSNE for asm data with perplexity 50
4.2.3 Conclusion on EDA
- 1. Class 3 can be easily separated because of the frequency of segments,opcodes and keywords being less
- 2. Each feature has its unique importance in separating the Class labels.
4.3 Train and test split
In [18]:
asm_y = result_asm['Class']
asm_x = result_asm.drop(['ID','Class','.BSS:','rtn','.CODE'], axis=1)
In [19]:
X_train_asm, X_test_asm, y_train_asm, y_test_asm = train_test_split(asm_x,asm_y ,stratify=asm_y,test_size=0.20)
X_train_asm, X_cv_asm, y_train_asm, y_cv_asm = train_test_split(X_train_asm, y_train_asm,stratify=y_train_asm,test_size=0.20)
In [20]:
print( X_cv_asm.isnull().all())
4.4. Machine Learning models on features of .asm files
4.4.1 K-Nearest Neigbors
In [35]:
# find more about KNeighborsClassifier() here http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html
# -------------------------
# default parameter
# KNeighborsClassifier(n_neighbors=5, weights=’uniform’, algorithm=’auto’, leaf_size=30, p=2,
# metric=’minkowski’, metric_params=None, n_jobs=1, **kwargs)
# methods of
# fit(X, y) : Fit the model using X as training data and y as target values
# predict(X):Predict the class labels for the provided data
# predict_proba(X):Return probability estimates for the test data X.
#-------------------------------------
# video link: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/k-nearest-neighbors-geometric-intuition-with-a-toy-example-1/
#-------------------------------------
# find more about CalibratedClassifier
#CV here at http://scikit-learn.org/stable/modules/generated/sklearn.calibration.CalibratedClassifierCV.html
# ----------------------------
# default paramters
# sklearn.calibration.CalibratedClassifierCV(base_estimator=None, method=’sigmoid’, cv=3)
#
# some of the methods of CalibratedClassifierCV()
# fit(X, y[, sample_weight]) Fit the calibrated model
# get_params([deep]) Get parameters for this estimator.
# predict(X) Predict the target of new samples.
# predict_proba(X) Posterior probabilities of classification
#-------------------------------------
# video link:
#-------------------------------------
alpha = [x for x in range(1, 21,2)]
cv_log_error_array=[]
for i in alpha:
k_cfl=KNeighborsClassifier(n_neighbors=i)
k_cfl.fit(X_train_asm,y_train_asm)
sig_clf = CalibratedClassifierCV(k_cfl, method="sigmoid")
sig_clf.fit(X_train_asm, y_train_asm)
predict_y = sig_clf.predict_proba(X_cv_asm)
cv_log_error_array.append(log_loss(y_cv_asm, predict_y, labels=k_cfl.classes_, eps=1e-15))
for i in range(len(cv_log_error_array)):
print ('log_loss for k = ',alpha[i],'is',cv_log_error_array[i])
best_alpha = np.argmin(cv_log_error_array)
fig, ax = plt.subplots()
ax.plot(alpha, cv_log_error_array,c='g')
for i, txt in enumerate(np.round(cv_log_error_array,3)):
ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))
plt.grid()
plt.title("Cross Validation Error for each alpha")
plt.xlabel("Alpha i's")
plt.ylabel("Error measure")
plt.show()
k_cfl=KNeighborsClassifier(n_neighbors=alpha[best_alpha])
k_cfl.fit(X_train_asm,y_train_asm)
sig_clf = CalibratedClassifierCV(k_cfl, method="sigmoid")
sig_clf.fit(X_train_asm, y_train_asm)
pred_y=sig_clf.predict(X_test_asm)
predict_y = sig_clf.predict_proba(X_train_asm)
print ('log loss for train data',log_loss(y_train_asm, predict_y))
predict_y = sig_clf.predict_proba(X_cv_asm)
print ('log loss for cv data',log_loss(y_cv_asm, predict_y))
predict_y = sig_clf.predict_proba(X_test_asm)
print ('log loss for test data',log_loss(y_test_asm, predict_y))
plot_confusion_matrix(y_test_asm,sig_clf.predict(X_test_asm))
4.4.2 Logistic Regression
In [36]:
# read more about SGDClassifier() at http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html
# ------------------------------
# default parameters
# SGDClassifier(loss=’hinge’, penalty=’l2’, alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=None, tol=None,
# shuffle=True, verbose=0, epsilon=0.1, n_jobs=1, random_state=None, learning_rate=’optimal’, eta0=0.0, power_t=0.5,
# class_weight=None, warm_start=False, average=False, n_iter=None)
# some of methods
# fit(X, y[, coef_init, intercept_init, …]) Fit linear model with Stochastic Gradient Descent.
# predict(X) Predict class labels for samples in X.
#-------------------------------
# video link: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/geometric-intuition-1/
#------------------------------
alpha = [10 ** x for x in range(-5, 4)]
cv_log_error_array=[]
for i in alpha:
logisticR=LogisticRegression(penalty='l2',C=i,class_weight='balanced')
logisticR.fit(X_train_asm,y_train_asm)
sig_clf = CalibratedClassifierCV(logisticR, method="sigmoid")
sig_clf.fit(X_train_asm, y_train_asm)
predict_y = sig_clf.predict_proba(X_cv_asm)
cv_log_error_array.append(log_loss(y_cv_asm, predict_y, labels=logisticR.classes_, eps=1e-15))
for i in range(len(cv_log_error_array)):
print ('log_loss for c = ',alpha[i],'is',cv_log_error_array[i])
best_alpha = np.argmin(cv_log_error_array)
fig, ax = plt.subplots()
ax.plot(alpha, cv_log_error_array,c='g')
for i, txt in enumerate(np.round(cv_log_error_array,3)):
ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))
plt.grid()
plt.title("Cross Validation Error for each alpha")
plt.xlabel("Alpha i's")
plt.ylabel("Error measure")
plt.show()
logisticR=LogisticRegression(penalty='l2',C=alpha[best_alpha],class_weight='balanced')
logisticR.fit(X_train_asm,y_train_asm)
sig_clf = CalibratedClassifierCV(logisticR, method="sigmoid")
sig_clf.fit(X_train_asm, y_train_asm)
predict_y = sig_clf.predict_proba(X_train_asm)
print ('log loss for train data',(log_loss(y_train_asm, predict_y, labels=logisticR.classes_, eps=1e-15)))
predict_y = sig_clf.predict_proba(X_cv_asm)
print ('log loss for cv data',(log_loss(y_cv_asm, predict_y, labels=logisticR.classes_, eps=1e-15)))
predict_y = sig_clf.predict_proba(X_test_asm)
print ('log loss for test data',(log_loss(y_test_asm, predict_y, labels=logisticR.classes_, eps=1e-15)))
plot_confusion_matrix(y_test_asm,sig_clf.predict(X_test_asm))
4.4.3 Random Forest Classifier
In [37]:
# --------------------------------
# default parameters
# sklearn.ensemble.RandomForestClassifier(n_estimators=10, criterion=’gini’, max_depth=None, min_samples_split=2,
# min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=’auto’, max_leaf_nodes=None, min_impurity_decrease=0.0,
# min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False,
# class_weight=None)
# Some of methods of RandomForestClassifier()
# fit(X, y, [sample_weight]) Fit the SVM model according to the given training data.
# predict(X) Perform classification on samples in X.
# predict_proba (X) Perform classification on samples in X.
# some of attributes of RandomForestClassifier()
# feature_importances_ : array of shape = [n_features]
# The feature importances (the higher, the more important the feature).
# --------------------------------
# video link: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/random-forest-and-their-construction-2/
# --------------------------------
alpha=[10,50,100,500,1000,2000,3000]
cv_log_error_array=[]
for i in alpha:
r_cfl=RandomForestClassifier(n_estimators=i,random_state=42,n_jobs=-1)
r_cfl.fit(X_train_asm,y_train_asm)
sig_clf = CalibratedClassifierCV(r_cfl, method="sigmoid")
sig_clf.fit(X_train_asm, y_train_asm)
predict_y = sig_clf.predict_proba(X_cv_asm)
cv_log_error_array.append(log_loss(y_cv_asm, predict_y, labels=r_cfl.classes_, eps=1e-15))
for i in range(len(cv_log_error_array)):
print ('log_loss for c = ',alpha[i],'is',cv_log_error_array[i])
best_alpha = np.argmin(cv_log_error_array)
fig, ax = plt.subplots()
ax.plot(alpha, cv_log_error_array,c='g')
for i, txt in enumerate(np.round(cv_log_error_array,3)):
ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))
plt.grid()
plt.title("Cross Validation Error for each alpha")
plt.xlabel("Alpha i's")
plt.ylabel("Error measure")
plt.show()
r_cfl=RandomForestClassifier(n_estimators=alpha[best_alpha],random_state=42,n_jobs=-1)
r_cfl.fit(X_train_asm,y_train_asm)
sig_clf = CalibratedClassifierCV(r_cfl, method="sigmoid")
sig_clf.fit(X_train_asm, y_train_asm)
predict_y = sig_clf.predict_proba(X_train_asm)
print ('log loss for train data',(log_loss(y_train_asm, predict_y, labels=sig_clf.classes_, eps=1e-15)))
predict_y = sig_clf.predict_proba(X_cv_asm)
print ('log loss for cv data',(log_loss(y_cv_asm, predict_y, labels=sig_clf.classes_, eps=1e-15)))
predict_y = sig_clf.predict_proba(X_test_asm)
print ('log loss for test data',(log_loss(y_test_asm, predict_y, labels=sig_clf.classes_, eps=1e-15)))
plot_confusion_matrix(y_test_asm,sig_clf.predict(X_test_asm))
4.4.4 XgBoost Classifier
In [38]:
# Training a hyper-parameter tuned Xg-Boost regressor on our train data
# find more about XGBClassifier function here http://xgboost.readthedocs.io/en/latest/python/python_api.html?#xgboost.XGBClassifier
# -------------------------
# default paramters
# class xgboost.XGBClassifier(max_depth=3, learning_rate=0.1, n_estimators=100, silent=True,
# objective='binary:logistic', booster='gbtree', n_jobs=1, nthread=None, gamma=0, min_child_weight=1,
# max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1,
# scale_pos_weight=1, base_score=0.5, random_state=0, seed=None, missing=None, **kwargs)
# some of methods of RandomForestRegressor()
# fit(X, y, sample_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None)
# get_params([deep]) Get parameters for this estimator.
# predict(data, output_margin=False, ntree_limit=0) : Predict with data. NOTE: This function is not thread safe.
# get_score(importance_type='weight') -> get the feature importance
# -----------------------
# video link2: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/what-are-ensembles/
# -----------------------
alpha=[10,50,100,500,1000,2000,3000]
cv_log_error_array=[]
for i in alpha:
x_cfl=XGBClassifier(n_estimators=i,nthread=-1)
x_cfl.fit(X_train_asm,y_train_asm)
sig_clf = CalibratedClassifierCV(x_cfl, method="sigmoid")
sig_clf.fit(X_train_asm, y_train_asm)
predict_y = sig_clf.predict_proba(X_cv_asm)
cv_log_error_array.append(log_loss(y_cv_asm, predict_y, labels=x_cfl.classes_, eps=1e-15))
for i in range(len(cv_log_error_array)):
print ('log_loss for c = ',alpha[i],'is',cv_log_error_array[i])
best_alpha = np.argmin(cv_log_error_array)
fig, ax = plt.subplots()
ax.plot(alpha, cv_log_error_array,c='g')
for i, txt in enumerate(np.round(cv_log_error_array,3)):
ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))
plt.grid()
plt.title("Cross Validation Error for each alpha")
plt.xlabel("Alpha i's")
plt.ylabel("Error measure")
plt.show()
x_cfl=XGBClassifier(n_estimators=alpha[best_alpha],nthread=-1)
x_cfl.fit(X_train_asm,y_train_asm)
sig_clf = CalibratedClassifierCV(x_cfl, method="sigmoid")
sig_clf.fit(X_train_asm, y_train_asm)
predict_y = sig_clf.predict_proba(X_train_asm)
print ('For values of best alpha = ', alpha[best_alpha], "The train log loss is:",log_loss(y_train_asm, predict_y))
predict_y = sig_clf.predict_proba(X_cv_asm)
print('For values of best alpha = ', alpha[best_alpha], "The cross validation log loss is:",log_loss(y_cv_asm, predict_y))
predict_y = sig_clf.predict_proba(X_test_asm)
print('For values of best alpha = ', alpha[best_alpha], "The test log loss is:",log_loss(y_test_asm, predict_y))
plot_confusion_matrix(y_test_asm,sig_clf.predict(X_test_asm))
4.4.5 Xgboost Classifier with best hyperparameters
In [39]:
x_cfl=XGBClassifier()
prams={
'learning_rate':[0.01,0.03,0.05,0.1,0.15,0.2],
'n_estimators':[100,200,500,1000,2000],
'max_depth':[3,5,10],
'colsample_bytree':[0.1,0.3,0.5,1],
'subsample':[0.1,0.3,0.5,1]
}
random_cfl=RandomizedSearchCV(x_cfl,param_distributions=prams,verbose=10,n_jobs=-1,)
random_cfl.fit(X_train_asm,y_train_asm)
Out[39]:
In [40]:
print (random_cfl.best_params_)
In [42]:
# Training a hyper-parameter tuned Xg-Boost regressor on our train data
# find more about XGBClassifier function here http://xgboost.readthedocs.io/en/latest/python/python_api.html?#xgboost.XGBClassifier
# -------------------------
# default paramters
# class xgboost.XGBClassifier(max_depth=3, learning_rate=0.1, n_estimators=100, silent=True,
# objective='binary:logistic', booster='gbtree', n_jobs=1, nthread=None, gamma=0, min_child_weight=1,
# max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1,
# scale_pos_weight=1, base_score=0.5, random_state=0, seed=None, missing=None, **kwargs)
# some of methods of RandomForestRegressor()
# fit(X, y, sample_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None)
# get_params([deep]) Get parameters for this estimator.
# predict(data, output_margin=False, ntree_limit=0) : Predict with data. NOTE: This function is not thread safe.
# get_score(importance_type='weight') -> get the feature importance
# -----------------------
# video link2: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/what-are-ensembles/
# -----------------------
x_cfl=XGBClassifier(n_estimators=2000,subsample=0.5,learning_rate=0.01,colsample_bytree=0.5,max_depth=10)
x_cfl.fit(X_train_asm,y_train_asm)
c_cfl=CalibratedClassifierCV(x_cfl,method='sigmoid')
c_cfl.fit(X_train_asm,y_train_asm)
predict_y = c_cfl.predict_proba(X_train_asm)
print ('train loss',log_loss(y_train_asm, predict_y))
predict_y = c_cfl.predict_proba(X_cv_asm)
print ('cv loss',log_loss(y_cv_asm, predict_y))
predict_y = c_cfl.predict_proba(X_test_asm)
print ('test loss',log_loss(y_test_asm, predict_y))
4.5. Machine Learning models on features of both .asm and .bytes files
4.5.1. Merging both asm and byte file features
In [21]:
result.head()
Out[21]:
In [22]:
result_asm.head()
Out[22]:
In [23]:
print(result.shape)
print(result_asm.shape)
In [24]:
result_x = pd.merge(result,result_asm.drop(['Class'], axis=1),on='ID', how='left')
result_y = result_x['Class']
result_x = result_x.drop(['ID','rtn','.BSS:','.CODE','Class'], axis=1)
result_x.head()
Out[24]:
4.5.2. Multivariate Analysis on final fearures
In [25]:
xtsne=TSNE(perplexity=50)
results=xtsne.fit_transform(result_x)
vis_x = results[:, 0]
vis_y = results[:, 1]
plt.scatter(vis_x, vis_y, c=result_y, cmap=plt.cm.get_cmap("jet", 9))
plt.colorbar(ticks=range(9))
plt.clim(0.5, 9)
plt.show()
4.5.3. Train and Test split
In [26]:
X_train, X_test_merge, y_train, y_test_merge = train_test_split(result_x, result_y,stratify=result_y,test_size=0.20)
X_train_merge, X_cv_merge, y_train_merge, y_cv_merge = train_test_split(X_train, y_train,stratify=y_train,test_size=0.20)
4.5.4. Random Forest Classifier on final features
In [34]:
# --------------------------------
# default parameters
# sklearn.ensemble.RandomForestClassifier(n_estimators=10, criterion=’gini’, max_depth=None, min_samples_split=2,
# min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=’auto’, max_leaf_nodes=None, min_impurity_decrease=0.0,
# min_impurity_split=None, bootstrap=True, oob_score=False, n_jobs=1, random_state=None, verbose=0, warm_start=False,
# class_weight=None)
# Some of methods of RandomForestClassifier()
# fit(X, y, [sample_weight]) Fit the SVM model according to the given training data.
# predict(X) Perform classification on samples in X.
# predict_proba (X) Perform classification on samples in X.
# some of attributes of RandomForestClassifier()
# feature_importances_ : array of shape = [n_features]
# The feature importances (the higher, the more important the feature).
# --------------------------------
# video link: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/random-forest-and-their-construction-2/
# --------------------------------
alpha=[10,50,100,500,1000,2000,3000]
cv_log_error_array=[]
from sklearn.ensemble import RandomForestClassifier
for i in alpha:
r_cfl=RandomForestClassifier(n_estimators=i,random_state=42,n_jobs=-1)
r_cfl.fit(X_train_merge,y_train_merge)
sig_clf = CalibratedClassifierCV(r_cfl, method="sigmoid")
sig_clf.fit(X_train_merge, y_train_merge)
predict_y = sig_clf.predict_proba(X_cv_merge)
cv_log_error_array.append(log_loss(y_cv_merge, predict_y, labels=r_cfl.classes_, eps=1e-15))
for i in range(len(cv_log_error_array)):
print ('log_loss for c = ',alpha[i],'is',cv_log_error_array[i])
best_alpha = np.argmin(cv_log_error_array)
fig, ax = plt.subplots()
ax.plot(alpha, cv_log_error_array,c='g')
for i, txt in enumerate(np.round(cv_log_error_array,3)):
ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))
plt.grid()
plt.title("Cross Validation Error for each alpha")
plt.xlabel("Alpha i's")
plt.ylabel("Error measure")
plt.show()
r_cfl=RandomForestClassifier(n_estimators=alpha[best_alpha],random_state=42,n_jobs=-1)
r_cfl.fit(X_train_merge,y_train_merge)
sig_clf = CalibratedClassifierCV(r_cfl, method="sigmoid")
sig_clf.fit(X_train_merge, y_train_merge)
predict_y = sig_clf.predict_proba(X_train_merge)
print ('For values of best alpha = ', alpha[best_alpha], "The train log loss is:",log_loss(y_train_merge, predict_y))
predict_y = sig_clf.predict_proba(X_cv_merge)
print('For values of best alpha = ', alpha[best_alpha], "The cross validation log loss is:",log_loss(y_cv_merge, predict_y))
predict_y = sig_clf.predict_proba(X_test_merge)
print('For values of best alpha = ', alpha[best_alpha], "The test log loss is:",log_loss(y_test_merge, predict_y))
4.5.5. XgBoost Classifier on final features
In [35]:
# Training a hyper-parameter tuned Xg-Boost regressor on our train data
# find more about XGBClassifier function here http://xgboost.readthedocs.io/en/latest/python/python_api.html?#xgboost.XGBClassifier
# -------------------------
# default paramters
# class xgboost.XGBClassifier(max_depth=3, learning_rate=0.1, n_estimators=100, silent=True,
# objective='binary:logistic', booster='gbtree', n_jobs=1, nthread=None, gamma=0, min_child_weight=1,
# max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1,
# scale_pos_weight=1, base_score=0.5, random_state=0, seed=None, missing=None, **kwargs)
# some of methods of RandomForestRegressor()
# fit(X, y, sample_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None)
# get_params([deep]) Get parameters for this estimator.
# predict(data, output_margin=False, ntree_limit=0) : Predict with data. NOTE: This function is not thread safe.
# get_score(importance_type='weight') -> get the feature importance
# -----------------------
# video link2: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/what-are-ensembles/
# -----------------------
alpha=[10,50,100,500,1000,2000,3000]
cv_log_error_array=[]
for i in alpha:
x_cfl=XGBClassifier(n_estimators=i)
x_cfl.fit(X_train_merge,y_train_merge)
sig_clf = CalibratedClassifierCV(x_cfl, method="sigmoid")
sig_clf.fit(X_train_merge, y_train_merge)
predict_y = sig_clf.predict_proba(X_cv_merge)
cv_log_error_array.append(log_loss(y_cv_merge, predict_y, labels=x_cfl.classes_, eps=1e-15))
for i in range(len(cv_log_error_array)):
print ('log_loss for c = ',alpha[i],'is',cv_log_error_array[i])
best_alpha = np.argmin(cv_log_error_array)
fig, ax = plt.subplots()
ax.plot(alpha, cv_log_error_array,c='g')
for i, txt in enumerate(np.round(cv_log_error_array,3)):
ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))
plt.grid()
plt.title("Cross Validation Error for each alpha")
plt.xlabel("Alpha i's")
plt.ylabel("Error measure")
plt.show()
x_cfl=XGBClassifier(n_estimators=3000,nthread=-1)
x_cfl.fit(X_train_merge,y_train_merge,verbose=True)
sig_clf = CalibratedClassifierCV(x_cfl, method="sigmoid")
sig_clf.fit(X_train_merge, y_train_merge)
predict_y = sig_clf.predict_proba(X_train_merge)
print ('For values of best alpha = ', alpha[best_alpha], "The train log loss is:",log_loss(y_train_merge, predict_y))
predict_y = sig_clf.predict_proba(X_cv_merge)
print('For values of best alpha = ', alpha[best_alpha], "The cross validation log loss is:",log_loss(y_cv_merge, predict_y))
predict_y = sig_clf.predict_proba(X_test_merge)
print('For values of best alpha = ', alpha[best_alpha], "The test log loss is:",log_loss(y_test_merge, predict_y))
4.5.5. XgBoost Classifier on final features with best hyper parameters using Random search
In [36]:
x_cfl=XGBClassifier()
prams={
'learning_rate':[0.01,0.03,0.05,0.1,0.15,0.2],
'n_estimators':[100,200,500,1000,2000],
'max_depth':[3,5,10],
'colsample_bytree':[0.1,0.3,0.5,1],
'subsample':[0.1,0.3,0.5,1]
}
random_cfl=RandomizedSearchCV(x_cfl,param_distributions=prams,verbose=10,n_jobs=-1,)
random_cfl.fit(X_train_merge, y_train_merge)
Out[36]:
In [37]:
print (random_cfl.best_params_)
In [39]:
# find more about XGBClassifier function here http://xgboost.readthedocs.io/en/latest/python/python_api.html?#xgboost.XGBClassifier
# -------------------------
# default paramters
# class xgboost.XGBClassifier(max_depth=3, learning_rate=0.1, n_estimators=100, silent=True,
# objective='binary:logistic', booster='gbtree', n_jobs=1, nthread=None, gamma=0, min_child_weight=1,
# max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1, reg_alpha=0, reg_lambda=1,
# scale_pos_weight=1, base_score=0.5, random_state=0, seed=None, missing=None, **kwargs)
# some of methods of RandomForestRegressor()
# fit(X, y, sample_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=None)
# get_params([deep]) Get parameters for this estimator.
# predict(data, output_margin=False, ntree_limit=0) : Predict with data. NOTE: This function is not thread safe.
# get_score(importance_type='weight') -> get the feature importance
# -----------------------
# video link2: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/what-are-ensembles/
# -----------------------
x_cfl=XGBClassifier(n_estimators=1000,max_depth=5,learning_rate=0.1,colsample_bytree=0.5,subsample=1,nthread=-1)
x_cfl.fit(X_train_merge,y_train_merge,verbose=True)
sig_clf = CalibratedClassifierCV(x_cfl, method="sigmoid")
sig_clf.fit(X_train_merge, y_train_merge)
predict_y = sig_clf.predict_proba(X_train_merge)
print ('For values of best alpha = ', alpha[best_alpha], "The train log loss is:",log_loss(y_train_merge, predict_y))
predict_y = sig_clf.predict_proba(X_cv_merge)
print('For values of best alpha = ', alpha[best_alpha], "The cross validation log loss is:",log_loss(y_cv_merge, predict_y))
predict_y = sig_clf.predict_proba(X_test_merge)
print('For values of best alpha = ', alpha[best_alpha], "The test log loss is:",log_loss(y_test_merge, predict_y))
plot_confusion_matrix(y_test_asm,sig_clf.predict(X_test_merge))
5. Assignments
- Add bi-grams on byte files and asm files and improve the log-loss to 0.01
- Watch the video (video) and include pixel intensity features to improve the logloss
Creating bi-grams and n-gram features on byte files and asm files¶
On Byte files¶
In [ ]:
'''#http://www.albertauyeung.com/post/generating-ngrams-python/
def generate_ngrams(s, n):
# Convert to lowercases
s = s.lower()
# Break sentence in the token, remove empty tokens
tokens = [token for token in s.split(",") if token != ""]
# Use the zip function to help us generate n-grams
# Concatentate the tokens into ngrams and return
ngrams = zip(*[tokens[i:] for i in range(n)])
return [" ".join(ngram) for ngram in ngrams]'''
In [23]:
byte_vocab = "00,01,02,03,04,05,06,07,08,09,0a,0b,0c,0d,0e,0f,10,11,12,13,14,15,16,17,18,19,1a,1b,1c,1d,1e,1f,20,21,22,23,24,25,26,27,28,29,2a,2b,2c,2d,2e,2f,30,31,32,33,34,35,36,37,38,39,3a,3b,3c,3d,3e,3f,40,41,42,43,44,45,46,47,48,49,4a,4b,4c,4d,4e,4f,50,51,52,53,54,55,56,57,58,59,5a,5b,5c,5d,5e,5f,60,61,62,63,64,65,66,67,68,69,6a,6b,6c,6d,6e,6f,70,71,72,73,74,75,76,77,78,79,7a,7b,7c,7d,7e,7f,80,81,82,83,84,85,86,87,88,89,8a,8b,8c,8d,8e,8f,90,91,92,93,94,95,96,97,98,99,9a,9b,9c,9d,9e,9f,a0,a1,a2,a3,a4,a5,a6,a7,a8,a9,aa,ab,ac,ad,ae,af,b0,b1,b2,b3,b4,b5,b6,b7,b8,b9,ba,bb,bc,bd,be,bf,c0,c1,c2,c3,c4,c5,c6,c7,c8,c9,ca,cb,cc,cd,ce,cf,d0,d1,d2,d3,d4,d5,d6,d7,d8,d9,da,db,dc,dd,de,df,e0,e1,e2,e3,e4,e5,e6,e7,e8,e9,ea,eb,ec,ed,ee,ef,f0,f1,f2,f3,f4,f5,f6,f7,f8,f9,fa,fb,fc,fd,fe,ff,??"
In [26]:
byte_bigram_vocab = []
for i, v in enumerate(byte_vocab.split(',')):
for j in range(0, len(byte_vocab.split(','))):
byte_bigram_vocab.append(v + ' ' +byte_vocab.split(',')[j])
len(byte_bigram_vocab)
Out[26]:
In [27]:
byte_bigram_vocab[:5]
Out[27]:
In [6]:
byte_trigram_vocab = []
for i, v in enumerate(byte_vocab.split(',')):
for j in range(0, len(byte_vocab.split(','))):
for k in range(0, len(byte_vocab.split(','))):
byte_trigram_vocab.append(v + ' ' +byte_vocab.split(',')[j]+' '+byte_vocab.split(',')[k])
len(byte_trigram_vocab)
Out[6]:
In [7]:
byte_trigram_vocab[:5]
Out[7]:
In [28]:
import scipy
from sklearn.feature_extraction.text import CountVectorizer
vector = CountVectorizer(lowercase=False,ngram_range=(2,2), vocabulary=byte_bigram_vocab)
bytebigram_vect = scipy.sparse.csr_matrix((10868, 66049))
for i, file in enumerate(os.listdir('byteFiles')):
f = open('byteFiles/' + file)
bytebigram_vect[i,:]+= scipy.sparse.csr_matrix(vect.fit_transform([f.read().replace('\n', ' ').lower()]))
f.close()
In [39]:
from sklearn.preprocessing import normalize
byte_bigram_vect = normalize(byte_bigram_vect)
bytebigram_vect
Out[39]:
On asm files {N-Gram(2-gram, 3-gram, 4-gram) Opcodes}¶
In [31]:
opcodes = ['jmp', 'mov', 'retf', 'push', 'pop', 'xor', 'retn', 'nop', 'sub', 'inc', 'dec', 'add','imul', 'xchg', 'or', 'shr', 'cmp', 'call', 'shl', 'ror', 'rol', 'jnb','jz','rtn','lea','movzx']
In [32]:
asmopcodebigram = []
for i, v in enumerate(opcodes):
for j in range(0, len(opcodes)):
asmopcodebigram.append(v + ' ' + opcodes[j])
len(asmopcodebigram)
Out[32]:
In [33]:
asmopcodetrigram = []
for i, v in enumerate(opcodes):
for j in range(0, len(opcodes)):
for k in range(0, len(opcodes)):
asmopcodetrigram.append(v + ' ' + opcodes[j] + ' ' + opcodes[k])
len(asmopcodetrigram)
Out[33]:
In [34]:
asmopcodetetragram = []
for i, v in enumerate(opcodes):
for j in range(0, len(opcodes)):
for k in range(0, len(opcodes)):
for l in range(0, len(opcodes)):
asmopcodetetragram.append(v + ' ' + opcodes[j] + ' ' + opcodes[k] + ' ' + opcodes[l])
len(asmopcodetetragram)
Out[34]:
In [ ]:
op_file = open("opcode_file.txt", "w+")
for asmfile in os.listdir('asmFiles'):
opcode_str = ""
with codecs.open('asmFiles/' + asmfile, encoding='cp1252', errors ='replace') as fli:
for lines in fli:
line = lines.rstrip().split()
for li in line:
if li in opcodes:
opcode_str += li + ' '
op_file.write(opcode_str + "\n")
op_file.close()
In [47]:
vect_bi = CountVectorizer(ngram_range=(2, 2), vocabulary = asmopcodebigram)
opcodebivect = scipy.sparse.csr_matrix((10868, len(asmopcodebigram)))
for i in range(10868):
raw_opcode = open('opcode_file.txt').read().split('\n')
opcodebivect[i, :] += scipy.sparse.csr_matrix(vect_bi.transform([raw_opcode[i]]))
vect_tri = CountVectorizer(ngram_range=(3, 3), vocabulary = asmopcodetrigram)
opcodetrivect = scipy.sparse.csr_matrix((10868, len(asmopcodetrigram)))
for i in range(10868):
opcodetrivect[i, :] += scipy.sparse.csr_matrix(vect_tri.transform([raw_opcode[i]]))
vect_tra = CountVectorizer(ngram_range=(4, 4), vocabulary = asmopcodetetragram)
opcodetetravect = scipy.sparse.csr_matrix((10868, len(asmopcodetetragram)))
for i in range(10868):
opcodetetravect[i, :] += scipy.sparse.csr_matrix(vect_tra.transform([raw_opcode[i]]))
In [48]:
opcodebivect
Out[48]:
In [52]:
opcodetrivect
Out[52]:
In [55]:
opcodetetravect
Out[55]:
Feature extraction as image from asmfiles¶
In [35]:
#ref : https://www.youtube.com/watch?v=VLQTRlLGz5Y#t=13m11s
import array
for asmfile in os.listdir("asmFiles"):
filename = asmfile.split('.')[0]
file = codecs.open("asmFiles/" + asmfile, 'rb')
filelen = os.path.getsize("asmFiles/" + asmfile)
width = int(filelen ** 0.5)
rem = int(filelen / width)
arr = array.array('B')
arr.frombytes(file.read())
file.close()
reshaped = np.reshape(arr[:width * width], (width, width))
reshaped = np.uint8(reshaped)
scipy.misc.imsave('asm_image/' + filename + '.png',reshaped)
extracting the first 200 Image Pixels¶
In [38]:
import cv2
imagefeatures = np.zeros((10868, 200)) # Creating a null matrix of 200 size vectors.
for i, asmfile in enumerate(os.listdir("asmFiles")):
img = cv2.imread("asm_image/" + asmfile.split('.')[0] + '.png')
img_arr = img.flatten()[:200] #therefore extracting only 200 features.
imagefeatures[i, :] += img_arr
In [68]:
#extracting the column names of first 200 pixels.
imgfeatures_name = []
for i in range(200):
img_features_name.append('pix' + str(i))
#Normalizing the features.
imgdf = pd.DataFrame(normalize(imagefeatures, axis = 0), columns = imgfeatures_name)
imgdf['ID'] = result.ID
In [70]:
imgdf.head()
Out[70]:
extracting the important Features Using Random Forest¶
In [38]:
#ref:https://towardsdatascience.com/explaining-feature-importance-by-example-of-a-random-forest-d9166011959e
def imp_features_indices(data, features, keep):
rf = RandomForestClassifier(n_estimators = 100, n_jobs = -1)
rf.fit(data, result_y)
imp_feature_indx = np.argsort(rf.feature_importances_)[::-1]
imp_value = np.take(rf.feature_importances_, imp_feature_indx[:20])
imp_feature_name = np.take(features, imp_feature_indx[:20])
return imp_feature_indx[:keep]
In [ ]:
sns.set()
plt.figure(figsize = (10, 5))
ax = sns.barplot(x = imp_feature_name, y = imp_value)
ax.set_xticklabels(labels = imp_feature_name, rotation = 45)
sns.set_palette(reversed(sns.color_palette("husl", 10)), 10)
plt.title('Important Features')
plt.xlabel('Feature Names')
plt.ylabel('Importance')
extracting Important Feature on Opcode N_grams¶
In [44]:
op_bi_indxes = imp_features_indices(normalize(opcodebivect, axis = 0), asmopcodebigram, 200)
op_tri_indxes = imp_features_indices(normalize(opcodetrivect, axis = 0), asmopcodetrigram, 200)
op_tetra_indxes = imp_features_indices(normalize(opcodetetravect, axis = 0), asmopcodetetragram, 200)
In [45]:
op_bi_df = pd.SparseDataFrame(normalize(opcodebivect, axis = 0), columns = asmopcodebigram)
op_bi_df = op_bi_df.loc[:, np.intersect1d(op_bi_df.columns, np.take(asmopcodebigram, op_bi_indxes))]
op_tri_df = pd.SparseDataFrame(normalize(opcodetrivect, axis = 0), columns = asmopcodetrigram)
op_tri_df = op_tri_df.loc[:, np.intersect1d(op_tri_df.columns, np.take(asmopcodetrigram, op_tri_indxes))]
op_tetra_df = pd.SparseDataFrame(normalize(opcodetetravect, axis = 0), columns = asmopcodetetragram)
op_tetra_df = op_tetra_df.loc[:, np.intersect1d(op_tetra_df.columns, np.take(asmopcodetetragram, op_tetra_indxes))]
In [48]:
op_bi_df['ID'] = result.ID
op_bi_df.head()
Out[48]:
In [43]:
op_tri_df['ID'] = result.ID
op_tri_df.head()
Out[43]:
In [53]:
op_tetra_df['ID'] = result.ID
op_tetra_df.head()
Out[53]:
extracting Important Feature on Byte Bi-Gram¶
In [54]:
byte_bi_indxes = imp_features_indices(normalize(bytebigram_vect, axis = 0), byte_bigram_vocab, 300)
top_byte_bi = np.zeros((10868, 0))
for i in byte_bi_indxes:
sliced = bytebigram_vect[:, i].todense()
top_byte_bi = np.hstack([top_byte_bi, sliced])
byte_bi_df = pd.SparseDataFrame(top_byte_bi, columns = np.take(byte_bigram_vocab, byte_bi_indxes))
Advanced features¶
Concating 300 bytebigram,200 opcode bigram,200 opcode trigram,200 opcode tetragram ,first 200 image pixels¶
In [74]:
final_data = pd.concat([result_x, op_bi_df, op_tri_df, op_tetra_df, byte_bi_df,img_df], axis = 1, join = 'inner')
#final_data.head()
Train cv and test split 80% and 20%¶
In [37]:
x_train_final, x_test_final, y_train_final, y_test_final = train_test_split(final_data, result_y, stratify = result_y, test_size = 0.20)
x_trn_final, x_cv_final, y_trn_final, y_cv_final = train_test_split(x_train_final, y_train_final, stratify = y_train_final, test_size = 0.20)
Xgboost modelling with all the features¶
In [29]:
alpha=[10,100,1000,2000]
cv_log_error_array=[]
for i in alpha:
x_cfl=XGBClassifier(n_estimators=i)
x_cfl.fit(x_trn_final,y_trn_final)
sig_clf = CalibratedClassifierCV(x_cfl, method="sigmoid")
sig_clf.fit(x_trn_final, y_trn_final)
predict_y = sig_clf.predict_proba(x_cv_final)
cv_log_error_array.append(log_loss(y_cv_final, predict_y, labels=x_cfl.classes_, eps=1e-15))
for i in range(len(cv_log_error_array)):
print ('log_loss for c = ',alpha[i],'is',cv_log_error_array[i])
best_alpha = np.argmin(cv_log_error_array)
fig, ax = plt.subplots()
ax.plot(alpha, cv_log_error_array,c='g')
for i, txt in enumerate(np.round(cv_log_error_array,3)):
ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))
plt.grid()
plt.title("Cross Validation Error for each alpha")
plt.xlabel("Alpha i's")
plt.ylabel("Error measure")
plt.show()
In [84]:
x_cfl=XGBClassifier(n_estimators=2000,nthread=-1)
x_cfl.fit(x_trn_final,y_trn_final,verbose=True)
sig_clf = CalibratedClassifierCV(x_cfl, method="sigmoid")
sig_clf.fit(x_trn_final, y_trn_final)
predict_y = sig_clf.predict_proba(x_trn_final)
print ('For values of best alpha = ', alpha[best_alpha], "The train log loss is:",log_loss(y_trn_final, predict_y))
predict_y = sig_clf.predict_proba(x_cv_final)
print('For values of best alpha = ', alpha[best_alpha], "The cross validation log loss is:",log_loss(y_cv_final, predict_y))
predict_y = sig_clf.predict_proba(x_test_final)
print('For values of best alpha = ', alpha[best_alpha], "The test log loss is:",log_loss(y_test_final, predict_y))
Conclusion:¶
In [8]:
from prettytable import PrettyTable
p = PrettyTable()
p.field_names = ["Multiclass Classification Models",'Type of Feature_Files','Test Logloss']
p.add_row(["random","bytefiles","2.45"])
p.add_row(["Knn","bytefiles","0.48"])
p.add_row(["Logistic Regression","bytefiles","0.52"])
p.add_row(["Random Forest Classifier ","bytefiles","0.06"])
p.add_row(["XgBoost ","bytefiles","0.07"])
p.add_row(["knn","asmfiles","0.21"])
p.add_row(["Logistic Regression","asmfiles","0.38"])
p.add_row(["Random Forest Classifier ","asmfiles","0.03"])
p.add_row(["XgBoost ","asmfiles","0.04"])
p.add_row(["Random Forest Classifier ","bytefiles+asmfiles","0.04"])
p.add_row(["XgBoost ","bytefiles+asmfiles","0.02"])
p.add_row(["XgBoost ","bytefiles+asmfiles+advanced features","0.01"])
print(p)
- since the dataset is nearly 200GB, it is highly computational expensive(It took me nearly 8 days to get the above results with 8gb RAM laptop.).
- The whole dataset is having 2 types of files(ie. Byte files and asm files).Thus we seperated the both files and preprocessed with Unigram vectorization and finally trained models individually to find the impact of each feature on our problem.
- Then combined the both files and applied the ML models, results stated above.
- Now we extracted bigram vectorization on bytefiles and N_gram(2,3,4-grams) vectorization on important opcodes of asmfiles.
- Applied the ML models with xgboost multiclass classification. Finally got logloss 0.01
In [9]:
#references:
#https://medium.com/datadriveninvestor/what-ive-learned-microsoft-malware-prediction-competition-on-kaggle-3c8189dcc850
# https://www.youtube.com/watch?v=VLQTRlLGz5Y
#https://github.com/dchad/malware-detection
#https://github.com/sai977/microsoft-malware-detection
#https://www.kaggle.com/c/microsoft-malware-prediction/discussion/74639
#https://www.kaggle.com/c/microsoft-malware-prediction/discussion/74638
In [ ]: